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Extension of Partitional Clustering Methods for Handling Mixed Data

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NCBR-MCD2008.pdf (148.9Kb)
Date
2008
Dewey
Recherche opérationnelle
Sujet
mixed data; homogeneity degree; Pratitional clustering
DOI
http://dx.doi.org/10.1109/ICDMW.2008.85
Conference name
IEEE International Conference on Data Mining Workshops, 2008. ICDMW '08
Conference date
12-2008
Conference city
Pise
Conference country
Italie
Book title
IEEE International Conference on Data Mining Workshops, 2008. ICDMW '08. Proceedings
Publisher
IEEE
Year
2008
ISBN
978-0-7695-3503-6
URI
https://basepub.dauphine.fr/handle/123456789/13021
Collections
  • LAMSADE : Publications
Metadata
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Author
Naija, Yosr
Chakhar, Salem
Blibech, Kaouther
Robbana, Riadh
Type
Communication / Conférence
Item number of pages
257-266
Abstract (EN)
Clustering is an active research topic in data mining and different methods have been proposed in the literature. Most of these methods are based on the use of a distance measure defined either on numerical attributes or on categorical attributes. However, in fields such as road traffic and medicine, datasets are composed of numerical and categorical attributes. Recently, there have been several proposals to develop clustering methods that support mixed attributes. There are three basic categories of clustering methods: partitional methods, hierarchical methods and density-based methods. This paper proposes an extension of partitional clustering methods devoted to mixed attributes. The proposed extension looks to create several partitions by using numerical attributes-based clustering methods and then chooses the one that maximizes a measure---called ``homogeneity degree"---of these partitions according to categorical attributes.

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